Regional load aggregator-oriented transformer area user flexible load scheduling method

Abstract

According to the regional load aggregator-oriented transformer area user flexible load scheduling method provided by the invention, the temperature control load, the electric vehicle, the energy storage equipment and the like are optimally scheduled, so that the balance of a power grid and the maximization of economic benefits are realized. The method comprises the following steps: predicting the load demand of a transformer area user through modeling analysis, and formulating an optimal scheduling strategy in combination with external signals such as real-time electricity price and environment temperature; a home energy efficiency management system (HEMS), a vehicle-to-power grid (V2G) control platform and an intelligent energy management system (EMS) are utilized to dynamically dispatch and control various flexible loads. The implementation result shows that the method effectively reduces the electric charge of the user, improves the revenue of the aggregator, improves the stability of the power grid, and has remarkable practicability and popularization value.

Description

Technical Field

[0001] The present invention relates to the technical field of power system dispatching, and in particular to a flexible load dispatching method for users in a substation area facing a regional load aggregator. Background Art

[0002] With the continuous growth of power system load, especially the increase in the proportion of new energy access, the operation of the power grid faces greater volatility and uncertainty. In order to ensure the stable operation of the power grid and improve the economic benefits of the power system, flexible load scheduling technology has gradually become a research hotspot. The flexible loads of users in the substation, such as temperature control loads, electric vehicles and energy storage equipment, have significant regulation potential. Through reasonable scheduling strategies, load peak shaving and valley filling can be achieved, reducing users' electricity costs and improving the reliability of power system operation. Therefore, it is of great practical significance and application value to develop a flexible load scheduling method for users in the substation for regional load aggregators. Summary of the invention

[0003] The purpose of the present invention is to provide a flexible load scheduling method for regional users for regional load aggregators, which can achieve optimal load regulation in the region and ensure the stability and economy of the power system by optimizing the scheduling strategies of flexible loads such as temperature control loads, electric vehicles, and energy storage equipment.

[0004] The solution of the present invention to solve the above technical problems is as follows: A flexible load dispatching method for users in a station area facing a regional load aggregator, characterized in that the method comprises the following steps: S1. Classification and modeling of flexible loads: According to the load types of users in the substation area, the flexible loads are divided into three categories: temperature control loads, electric vehicles and energy storage equipment. Load models are established for each category, and relevant control objectives and constraints are defined. S2. Temperature-controlled load dispatching strategy: Based on the electricity price signal and the external ambient temperature, the operating state of the temperature-controlled load is adjusted through temperature control means, the time allocation of the load is optimized, the power consumption during the high electricity price period is reduced, the user cost is minimized and the grid auxiliary services are provided; S3. Orderly charging and discharging strategy for electric vehicles: Use centralized V2G technology (Vehicle-to-grid technology) to uniformly dispatch the charging and discharging of electric vehicles in the region, and formulate the optimal dispatching plan for charging and discharging power based on the charging time distribution characteristics of electric vehicles and the goal of maximizing the benefits of car owners; S4. Optimizing the scheduling strategy of energy storage equipment: Under the premise of meeting the peak load demand, considering the goals of maximizing the annual revenue of the aggregator and minimizing the peak-to-valley difference of the power consumption curve of the substation, optimize the configuration and scheduling strategy of energy storage equipment to maximize the economic benefits of the aggregator; S5. Joint dispatch of multiple flexible loads: Based on the above single load dispatch strategy, considering the time and power complementarity among multiple flexible loads, a joint dispatch strategy is proposed to further optimize the overall load of the substation and improve the safety and stability of system operation.

[0005] Based on the above technical solution, the present invention can also be improved as follows.

[0006] Furthermore, in the temperature control load scheduling strategy, the optimal temperature control strategy of the temperature control load is determined by an intelligent optimization algorithm to achieve load transfer during high electricity price periods.

[0007] Furthermore, in the orderly charging and discharging strategy for electric vehicles, centralized V2G technology is used to achieve unified scheduling and optimized power allocation of electric vehicles in the region.

[0008] Furthermore, in the energy storage device optimization scheduling strategy, the capacity configuration and scheduling strategy of the aggregation side energy storage device are determined by an optimization algorithm to achieve the goals of maximizing the economic benefits of the aggregator and minimizing the peak-to-valley difference of the electricity consumption curve.

[0009] Furthermore, in the multi-flexible load joint dispatching strategy, the time and power complementarity of different flexible loads are combined to optimize the dispatching of the overall load of the substation, thereby improving the operating efficiency of the power system in the region.

[0010] Furthermore, the method can reduce the electricity cost of users and bring higher economic benefits to load aggregators.

[0011] Furthermore, the temperature control load scheduling strategy in the method can achieve fine-grained regulation of the load by monitoring and predicting the trend of ambient temperature changes to ensure maximum user comfort.

[0012] Furthermore, the electric vehicle orderly charging and discharging strategy dynamically adjusts the charging and discharging plan based on the battery status of the electric vehicle, the load demand of the power grid and the fluctuation of electricity price to achieve the purpose of peak shaving and valley filling.

[0013] Furthermore, the multi-flexible load joint dispatching strategy optimizes the combined use of flexible loads through real-time data collection and analysis, maximizes the use of renewable energy resources in the substation, further reduces carbon emissions and improves the green environmental protection benefits of the system.

[0014] The present invention provides a flexible load dispatching method for regional users facing regional load aggregators. It has the following beneficial effects: Improve the stability of the power system: This invention can effectively deal with the load fluctuation problem in the power system by classifying and modeling different types of flexible loads in the substation area and implementing accurate load scheduling strategies in combination with the actual power grid operation conditions. This accurate scheduling enables the system to better adapt to the uncertainty brought about by the access of new energy sources, avoid the instability of the power grid caused by load fluctuations, and thus significantly improve the operational stability of the power system.

[0015] Reduce user electricity costs: The flexible load scheduling method in the present invention fully considers the electricity price signal and user electricity demand, and effectively controls electricity costs by increasing the use of flexible loads during low electricity price periods and reducing power consumption during high electricity price periods. For example, the temperature control load can increase the operating time during periods with lower electricity prices and reduce its use during periods with higher electricity prices. This dynamic adjustment strategy can help users significantly reduce overall electricity costs without affecting their electricity experience.

[0016] Improve the economic benefits of aggregators: Through the method of the present invention, load aggregators can achieve optimal scheduling of various flexible loads and improve their participation in the electricity market. In particular, in the optimal scheduling of energy storage equipment and electric vehicle charging and discharging, the present invention takes into account the aggregator's profit maximization goal, and through reasonable arrangement of the charging and discharging plan of energy storage equipment and the orderly charging and discharging of electric vehicles, it realizes the arbitrage of electricity trading and improves the economic benefits of aggregators. In addition, load aggregators can also obtain additional income by providing grid auxiliary services, further improving their profitability.

[0017] Promoting peak load reduction and valley filling: By adopting centralized V2G (vehicle to grid) technology, the present invention can coordinate and unify the charging and discharging behaviors of a large number of electric vehicles in the dispatching area. This technology can reversely transmit the power of electric vehicles back to the grid during the peak load period of the grid, helping to reduce the peak load; and during the trough period of the grid, low-priced electricity can be used to charge electric vehicles, thereby filling the valley load of the grid. The reasonable dispatch of energy storage equipment also plays a similar role. By storing and releasing electric energy, it can smooth the load curve of the grid, reduce grid fluctuations, and improve the operating efficiency of the grid.

[0018] Enhanced utilization of renewable energy: The multi-element flexible load joint dispatching strategy of the present invention can better optimize the utilization of renewable energy (such as solar energy, wind energy, etc.) in the substation area. For example, during the peak period of photovoltaic power generation, the utilization rate of renewable energy can be maximized by timely dispatching energy storage equipment to store excess electricity or mobilizing electric vehicles to charge. This not only reduces dependence on traditional fossil energy, but also reduces carbon emissions and improves the green environmental protection benefits of the system.

[0019] Improve user comfort: In the temperature control load scheduling strategy, the present invention combines the changes in external ambient temperature and accurately adjusts the operating status of the temperature control equipment through intelligent algorithms to ensure that the most suitable temperature environment is provided in different time periods. For example, during periods of low electricity prices, the temperature control equipment can be started in advance or the operating time can be extended to improve comfort; during periods of high electricity prices, the operating frequency of the equipment can be appropriately lowered, while maintaining comfort through pre-adjustment. This scheduling method reduces users' electricity bills while not affecting or even improving users' living and office comfort experience.

[0020] Improve system operation efficiency: The present invention not only optimizes the operation of a single load through the joint scheduling of multiple flexible loads, but also achieves the optimization effect of the overall area load through the time and power complementarity between loads. This overall optimization can reduce the waste of power resources and improve the overall operation efficiency of the power system. For example, during the peak charging period of electric vehicles, energy storage equipment can be scheduled to release electricity, while in other periods, temperature control loads can be mobilized to adjust the load demand of the power grid. Through such refined load management, the power system can operate more efficiently and intelligently, reducing the consumption of redundant resources.

[0021] Through the above beneficial effects, the present invention not only improves the reliability of the power system and the economic benefits of users, but also provides technical support for achieving sustainable development and reducing carbon emissions, laying a solid foundation for the future construction of smart grids. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The drawings described herein are used to provide further understanding of the present invention and constitute a part of this application. The illustrative embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0023] In the attached picture: Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] See also Figure 1 As shown, the embodiments provided by the present invention are: Embodiment 1

[0026] Implementation of temperature control load scheduling strategy In this embodiment, a temperature control load scheduling strategy based on electricity price signals and ambient temperature changes is proposed for the case where the main flexible loads in the substation are temperature control devices such as air conditioners and water heaters. By reasonably scheduling these temperature control loads, it is not only possible to reduce the user's electricity cost, but also to ensure the user's comfort and further smooth the grid load curve.

[0027] Load forecasting: First, by collecting historical electricity consumption data of users in the substation area, including the use of air conditioners and water heaters, and real-time ambient temperature data, the time series analysis method is used to process the historical electricity consumption data of users in the substation area, including the use records of air conditioners and water heaters, ambient temperature, etc. This method comprehensively considers seasonal changes and users' daily usage habits, and predicts the temperature control load demand in the next 24 hours by analyzing the data. The prediction results include the expected usage power and duration of air conditioners and water heaters in each period, and the prediction accuracy reaches more than 90%.

[0028] Electricity price signal acquisition: By connecting with the power company or the grid dispatch center, the electricity price fluctuation of the day is acquired and analyzed in real time, and the peak, flat and valley periods are identified. Electricity price signals are usually divided into three periods: peak, flat and valley. The peak period has the highest electricity price and the valley period has the lowest electricity price. The acquired electricity price information will be used as a key input parameter for load dispatching. Using historical electricity price data and weather forecast information, electricity price fluctuations are predicted with an error control of less than 5%.

[0029] Optimization scheduling algorithm: In order to minimize electricity costs, an optimization scheduling algorithm based on linear programming is designed based on electricity price signals and load forecasts. The goal of this algorithm is to ensure that the user comfort level is not less than 80%, while meeting the equipment operation characteristics, by increasing the use of air conditioners and water heaters during low electricity price periods and reducing electricity consumption during high electricity price periods, thereby reducing the overall electricity cost. The specific steps are as follows: During off-peak hours, advance or extend the operation time of air conditioners and water heaters to ensure full utilization of electricity when electricity prices are low.

[0030] During peak hours, reduce the frequency of air conditioning operation, appropriately increase the temperature setting, and reduce the frequency of water heater use.

[0031] During the off-peak hours, maintain normal operation and try to avoid running a large number of high-power loads during this period.

[0032] Load adjustment and execution: Through the smart home control system, the optimized scheduling plan is applied to temperature control equipment such as air conditioners and water heaters in the substation. Specific operations include remote control of the opening, closing and temperature setting of air conditioners, and automatic adjustment of the heating period of water heaters. These operations are all implemented through the home energy efficiency management system (HEMS), which can intelligently adjust the operating mode of each device according to the scheduling plan to achieve energy saving.

[0033] Effect evaluation and feedback: After the dispatch strategy is implemented, the electricity consumption of each user is monitored in real time through smart meters, and the actual electricity consumption and electricity bill expenditure are recorded and compared with the electricity cost before dispatch. The comparison results show that after the implementation of the dispatch strategy, the user's electricity bill has been reduced by an average of 15%-20%, and the peak-to-valley difference of the power grid load curve has been reduced by 10%. At the same time, the comfort scores collected through the user feedback system show that the quality of life of users has not been significantly affected. Based on these evaluation results, the dispatch algorithm is iteratively optimized to make it more in line with actual needs.

[0034] This implementation method not only reduces users' electricity costs through intelligent scheduling of temperature control loads, but also effectively alleviates the load pressure on the power grid, especially during peak electricity price periods, significantly reducing electricity consumption. Embodiment 2

[0035] Implementation of orderly charging and discharging strategy for electric vehicles In this embodiment, there are a large number of electric vehicles in the substation area, and the centralized V2G (vehicle to grid) technology can realize the orderly charging and discharging scheduling of electric vehicles. This implementation not only meets the travel needs of electric vehicle owners, but also brings additional benefits to car owners and aggregators through power trading, and realizes peak load shaving and valley filling of the power grid.

[0036] Charging and discharging demand forecast: First, based on the historical usage data of electric vehicles in the substation area, the charging and discharging demand of electric vehicles in the next 24 hours is predicted. The forecast data includes information such as the travel time, charging demand, and remaining battery power of each electric vehicle. In order to make more accurate predictions, machine learning algorithms are used for data analysis, and seasonal factors and special events (such as holidays) are combined to adjust the charging and discharging demand, and the prediction accuracy rate reaches more than 95%.

[0037] V2G dispatch strategy formulation: According to the predicted charging and discharging demand, grid load curve and real-time electricity price signal, the optimal charging and discharging dispatch strategy is formulated. The main goal of the strategy is to help the grid achieve peak load reduction and valley filling by scheduling the charging and discharging time of electric vehicles, while bringing benefits to car owners. Specific strategies include: Peak hours: During peak load periods (such as at night), the power of electric vehicles is transmitted back to the grid through V2G technology. During this period, electric vehicles act as a distributed energy storage device, releasing power to relieve grid pressure and receive compensation from the grid.

[0038] Valley period: During the low load period of the power grid (such as early morning), electric vehicles are charged in batches to store energy using low-priced electricity. This strategy not only helps car owners save charging costs, but also further fills the low load period of the power grid and avoids the waste of power resources.

[0039] Real-time dispatch and control: The centralized V2G control platform monitors the charging and discharging status of electric vehicles in the substation in real time. The control platform can dynamically adjust the charging and discharging plan according to the real-time electricity price fluctuations and grid load changes. For example, when there is a sudden load peak, the system can temporarily dispatch more electric vehicles for discharge to meet emergency needs; when the electricity price suddenly drops, the number of charging vehicles can be increased to make full use of low-priced electricity.

[0040] Revenue distribution and settlement: Based on the participation of electric vehicle owners in V2G services, the discharge volume of each vehicle and the service value contributed to the power grid are calculated. The system uses smart contracts to automatically settle users' revenue, and the average additional income per vehicle has increased by 10%-15%. The transparency and fairness of the revenue distribution system have been widely recognized by users. Car owners can not only reduce the charging costs of their vehicles through V2G services, but also obtain additional economic benefits, while aggregators make profits through service charges.

[0041] Effect evaluation: The effect of the implementation of the V2G scheduling strategy is evaluated, mainly examining the following aspects: Grid load curve smoothness: By analyzing the grid load curve, the effect of peak shaving and valley filling is evaluated. Generally, after implementing the V2G strategy, the peak-to-valley difference of the load curve is significantly reduced, and the grid operation is more stable.

[0042] · Electric vehicle battery life: Monitor the usage of electric vehicle batteries, evaluate the impact of frequent charging and discharging on battery life, and add battery protection mechanisms to the scheduling strategy to extend battery life.

[0043] Economic benefits to car owners: The economic benefits of car owners after participating in V2G services are evaluated through the profit distribution system, and feedback from car owners is collected to continuously optimize the profit distribution model.

[0044] This implementation method achieves effective regulation of grid load by optimizing the charging and discharging process of electric vehicles, while improving the economic benefits of car owners and aggregators, and promoting the widespread application of V2G technology in practice. Embodiment 3

[0045] Implementation of energy storage equipment optimization scheduling strategy In this embodiment, the energy storage equipment configured in the substation is optimized to maximize the economic benefits of the aggregator and minimize the peak-to-valley difference of the power grid by optimizing its charging and discharging strategy. This strategy can not only effectively cope with electricity price fluctuations, but also smooth the load curve of the power grid by reasonably dispatching energy storage equipment, thereby improving the overall operating efficiency of the power system.

[0046] Energy storage equipment modeling and demand analysis: First, a mathematical model of energy storage equipment is established based on the actual load conditions of the substation and the technical parameters of the energy storage equipment (such as maximum energy storage capacity, charging and discharging efficiency, life attenuation, etc.). Combined with historical data and grid dispatching requirements, the charging and discharging requirements of energy storage equipment in the next 24 hours are analyzed. The prediction results include the charging demand, discharge capacity and auxiliary service capabilities of energy storage equipment for each time period.

[0047] Scheduling target setting: Based on the characteristics of energy storage equipment, set scheduling targets. The main targets include: Maximizing the aggregator's profit: By charging during low-price periods and discharging during peak periods, electricity price arbitrage can be achieved to maximize the economic benefits of the aggregator.

[0048] Minimize the peak-to-valley difference of the power grid: By rationally scheduling the charging and discharging time of energy storage equipment, the load curve of the power grid can be smoothed, the peak-to-valley difference can be reduced, and the scheduling pressure of the power grid can be alleviated.

[0049] Optimization algorithm design: This implementation method uses a mixed integer linear programming (MILP) algorithm to optimize the charging and discharging process of the energy storage device. The optimization algorithm needs to take into account multiple factors, including electricity price fluctuations, the charging and discharging efficiency of the energy storage device, the life attenuation cost, and the load demand of the power grid. The algorithm maximizes economic benefits while meeting the life and power balance of the energy storage device. The optimized scheduling strategy shows that the benefits have increased by 10%-15%. The specific steps of the optimization algorithm are as follows: Variable setting: Define variables such as the charge amount, discharge amount, and energy storage state of the energy storage device in each period, while considering the power limit and power balance constraints of the energy storage device.

[0050] Objective function: The objective function is set to maximize the revenue of the aggregator, while adding the constraint of minimizing the peak-valley difference of the power grid. The revenue calculation of the aggregator is based on the difference in electricity prices, while the minimization of the peak-valley difference of the power grid is achieved by smoothing the load curve.

[0051] Constraints: The algorithm needs to consider the following constraints: Power constraint: The charging and discharging power of the energy storage device in each period cannot exceed its rated power.

[0052] ·Power balance constraint: The power change of energy storage equipment should comply with the laws of physics, that is, the storage capacity of the current period is equal to the storage capacity of the previous period plus the charge capacity minus the discharge capacity.

[0053] Battery life constraints: To extend the service life of energy storage devices, the algorithm introduces restrictions on the number and depth of charge and discharge to avoid excessive wear of the equipment due to frequent charge and discharge.

[0054] Dispatch execution and monitoring: The optimized dispatch strategy is executed through the intelligent energy management system (EMS). EMS monitors the operating status of energy storage equipment in real time, including key parameters such as charging and discharging current, voltage, and temperature, to ensure the safety and reliability of the dispatch process. At the same time, EMS adjusts the operation plan of energy storage equipment in a timely manner according to the dynamic changes in grid load. The discharge power control accuracy reaches more than 95%. For example, in the event of a sudden grid load peak, the discharge power can be temporarily increased to stabilize the grid operation; in the unexpected low electricity price period, the charging capacity is increased to reserve electricity.

[0055] Economic benefit evaluation: After the dispatch strategy has been implemented for a period of time, the actual operating data of the energy storage equipment is analyzed to evaluate its economic benefits and contribution to the power grid. Through a comprehensive evaluation of economic benefits and power grid operation, the results show that the aggregator's revenue has increased by 20% and the peak-to-valley difference of the power grid load has decreased by 12%. The life of the energy storage equipment has not been significantly affected, and the dispatch strategy has shown good practicality. The evaluation indicators include: Economic benefits: Calculate the electricity price arbitrage benefits obtained by energy storage equipment through charging and discharging, and analyze its contribution to the profits of aggregators.

[0056] Contribution to grid dispatching: Evaluate the contribution of energy storage equipment to peak load shaving and valley filling through changes in load curves, and analyze its improvement in grid operation stability.

[0057] Impact on equipment life: Evaluate the impact of scheduling strategies on the life of energy storage equipment based on equipment operation data, especially the impact of charge and discharge times and depth on battery life.

[0058] Feedback optimization: Based on the results of economic benefit evaluation, the scheduling algorithm is further optimized. For example, if it is found that the life of the energy storage equipment is declining rapidly, the charging and discharging strategy can be adjusted to reduce the single discharge depth or reduce the frequent charging and discharging operations to extend the life of the equipment; if the economic benefits do not meet expectations, the electricity price fluctuation model can be re-evaluated or the selection of scheduling periods can be optimized.

[0059] This implementation method achieves smoothing of grid load and maximization of aggregator revenue by optimizing the scheduling of energy storage equipment, thereby improving the operating efficiency of the power system and extending the service life of energy storage equipment.

[0060] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic features of the present invention. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the attached claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any figure mark in the claims should not be regarded as limiting the claims involved.

[0061] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A flexible load dispatching method for regional load aggregators, characterized in that: The method comprises the following steps: S1. Classification and modeling of flexible loads: According to the load types of users in the substation area, the flexible loads are divided into three categories: temperature control loads, electric vehicles and energy storage equipment. Load models are established for each category, and relevant control objectives and constraints are defined. S2. Temperature-controlled load dispatching strategy: Based on the electricity price signal and the external ambient temperature, the operating state of the temperature-controlled load is adjusted through temperature control means, the time allocation of the load is optimized, the power consumption during the high electricity price period is reduced, the user cost is minimized and the grid auxiliary services are provided; S3. Orderly charging and discharging strategy for electric vehicles: Use centralized V2G technology to uniformly dispatch the charging and discharging of electric vehicles in the region, and formulate the optimal scheduling plan for charging and discharging power based on the charging time distribution characteristics of electric vehicles and the goal of maximizing the owner's profit; S4. Optimizing the scheduling strategy of energy storage equipment: Under the premise of meeting the peak load demand, considering the goals of maximizing the annual revenue of the aggregator and minimizing the peak-to-valley difference of the power consumption curve of the substation, optimize the configuration and scheduling strategy of energy storage equipment to maximize the economic benefits of the aggregator; S5. Joint dispatch of multiple flexible loads: Based on the above single load dispatch strategy, considering the time and power complementarity among multiple flexible loads, a joint dispatch strategy is proposed to further optimize the overall load of the substation and improve the safety and stability of system operation.

2. According to claim 1, a flexible load dispatching method for regional load aggregators is characterized by: In the temperature control load scheduling strategy, the optimal temperature control strategy of the temperature control load is determined by an intelligent optimization algorithm to achieve load transfer during high electricity price periods.

3. According to the method of flexible load dispatching for regional load aggregators in accordance with claim 1, it is characterized by: In the orderly charging and discharging strategy for electric vehicles, centralized V2G technology is adopted to achieve unified scheduling and optimized power allocation of electric vehicles in the region.

4. According to the method of flexible load dispatching for regional load aggregators in accordance with claim 1, it is characterized by: In the energy storage device optimization scheduling strategy, the capacity configuration and scheduling strategy of the aggregation side energy storage device are determined by an optimization algorithm to achieve the goals of maximizing the economic benefits of the aggregator and minimizing the peak-to-valley difference of the electricity consumption curve.

5. According to the method of flexible load dispatching for regional load aggregators in accordance with claim 1, it is characterized by: In the multi-flexible load joint dispatching strategy, the time and power complementarity of different flexible loads are combined to optimize the dispatching of the overall load of the substation area and improve the operating efficiency of the power system in the region.

6. According to claim 1, a flexible load dispatching method for regional load aggregators is characterized by: The method can reduce the electricity cost of users and bring higher economic benefits to load aggregators.

7. According to claim 1, a flexible load dispatching method for regional load aggregators is characterized by: The temperature control load scheduling strategy in the method realizes fine adjustment of the load by monitoring and predicting the trend of ambient temperature changes to ensure maximum user comfort.

8. According to the method of flexible load dispatching for regional users of regional load aggregators as described in claim 1, it is characterized by: The electric vehicle orderly charging and discharging strategy dynamically adjusts the charging and discharging plan based on the battery status of the electric vehicle, the load demand of the power grid and the fluctuation of electricity price to achieve the purpose of peak shaving and valley filling.

9. The method for flexible load dispatching of users in a substation area for a regional load aggregator according to claim 1, characterized in that: The multi-flexible load joint dispatching strategy optimizes the combined use of flexible loads through real-time data collection and analysis, maximizes the use of renewable energy resources in the substation, further reduces carbon emissions and improves the green environmental protection benefits of the system.

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